1 citations · 2 across the 6 of their papers we have counts for
7 papers
A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks
Yang Zhao, Yue Dai, Liang Qiao +8
Temporal graph neural networks (TGNNs) achieve superior performance in many dynamic graph learning tasks. These models use node state vectors (i.e., memories) to capture and record…
Accelerating 3D Gaussian Splatting using Tensor Cores
Sheng Li, Yang Sui, Yue Wu +4
3D Gaussian Splatting (3DGS) has become a leading technique for real-time neural rendering and 3D scene reconstruction, but its rendering cost remains too high for many latency-sen…
Temporal Aware Pruning for Efficient Diffusion-based Video Generation
Sheng Li, Yang Sui, Junhao Ran +3
Video diffusion models have recently enabled high-quality video generation with ViT-based architectures, but remain computationally intensive because generation requires attention…
Using Reinforcement Learning to Guide Graph State Generation for Photonic Quantum Computers
Yingheng Li, Yue Dai, Aditya Pawar +4
Photonic quantum computer (PQC) is an emerging and promising quantum computing paradigm that has gained momentum in recent years. In PQC, which leverages the measurement-based quan…
Improving GPU Multi-Tenancy Through Dynamic Multi-Instance GPU Reconfiguration
Tianyu Wang, Sheng Li, Bingyao Li +6
Continuous learning (CL) has emerged as one of the most popular deep learning paradigms deployed in modern cloud GPUs. Specifically, CL has the capability to continuously update th…
SmartFRZ: An Efficient Training Framework using Attention-Based Layer Freezing
Sheng Li, Geng Yuan, Yue Dai +3
There has been a proliferation of artificial intelligence applications, where model training is key to promising high-quality services for these applications. However, the model tr…
EdgeOL: Efficient in-situ Online Learning on Edge Devices
Sheng Li, Geng Yuan, Yue Dai +10
Emerging applications, such as robot-assisted eldercare and object recognition, generally employ deep learning neural networks (DNNs) and naturally require: i) handling streaming-i…